We present a probabilistic method for path planning
that considers trajectories constrained by both the environment
and an ensemble of restrictions or preferences on preferred
motions for a moving robot. Our system learns constraints and
preference biases on a robots motion from examples, and then
synthesizes behaviors that satisfy these constraints. This behavior
can encompass motions that satisfy diverse requirements such
as a sweep pattern for floor coverage, or, in particular in
our experiments, satisfy restrictions on the robots physical
capabilities such as restrictions on its turning radius. Given an
approximate path that may not satisfy the required conditions,
our system computes a refined path that satisfies the constraints
and also avoids obstacles. Our approach is based on a Bayesian
framework for combining a prior probability distribution on the
trajectory with environmental constraints. The prior distribution
is generated by decoding a Hidden Markov Model, which is
itself is trained over a particular set of preferred motions.
Environmental constraints are modeled using a potential field
over the configuration space.
This paper poses the requisite theoretical framework and
demonstrates its effectiveness with several examples.